get all the total TFBS size

Download the maize B73 V4 data and sorghum genome file

wget ftp://ftp.ensemblgenomes.org/pub/plants/release-34/gff3/zea_mays/Zea_mays.AGPv4.34.gff3.gz
gunzip Zea_mays.AGPv4.34.gff3.gz
wget ftp://ftp.ensemblgenomes.org/pub/plants/release-34/fasta/zea_mays/dna/Zea_mays.AGPv4.dna.toplevel.fa.gz
gunzip Zea_mays.AGPv4.dna.toplevel.fa.gz
wget ftp://ftp.ensemblgenomes.org/pub/plants/release-49/fasta/sorghum_bicolor/dna/Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa.gz
gunzip Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa.gz

Using AnchorWave to extract full-length CDS.
NOTE: please do NOT use CDS extracted using other software and do NOT use the output full-length CDS file for other purpose. Since AnchorWave filtered some CDS records to minimum the impact of minimap2 limitation on genome alignment that “Minimap2 often misses small exons” (https://github.com/lh3/minimap2#limitations)

anchorwave gff2seq -r Zea_mays.AGPv4.dna.toplevel.fa -i Zea_mays.AGPv4.34.gff3 -o cds.fa

use minimap2 (https://github.com/lh3/minimap2) to map the extracted sequence to the reference genome sequence and synthesis genomes

minimap2 -x splice -t 11 -k 12 -a -p 0.4 -N 20 Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa cds.fa > cds.sam
minimap2 -x splice -t 11 -k 12 -a -p 0.4 -N 20 Zea_mays.AGPv4.dna.toplevel.fa  cds.fa > ref.sam

prepare genome annotation files
all_reproducible_peaks_summits_merged.bed has been published at https://www.nature.com/articles/s41467-020-18832-8

wget https://github.com/mcstitzer/maize_TEs/raw/master/B73.structuralTEv2.disjoined.2018-09-19.gff3.gz
gunzip B73.structuralTEv2.disjoined.2018-09-19.gff3.gz
grep -v "#" B73.structuralTEv2.disjoined.2018-09-19.gff3 | awk '{print $1"\t"$4-1"\t"$5}'  | bedtools sort | bedtools merge  > B73.structuralTEv2.disjoined.2018-09-19.bed

grep "biotype=protein_coding" Zea_mays.AGPv4.34.gff3 | grep -e "\sgene\s" | awk '{print $1"\t"$4-1"\t"$5}' | bedtools merge > Zea_mays.AGPv4.34_coding_gene.bed
#total length of geneitc region 
cat Zea_mays.AGPv4.34_coding_gene.bed | awk -F'\t' 'BEGIN{SUM=0}{ SUM+=$3-$2 }END{print SUM}' # 162494287
grep "\sCDS\s" Zea_mays.AGPv4.34.gff3 |  awk '{print $1"\t"$4-1"\t"$5}' | bedtools sort |  bedtools merge  > Zea_mays.AGPv4.34_CDS.bed
bedtools subtract  -a Zea_mays.AGPv4.34_coding_gene.bed -b Zea_mays.AGPv4.34_CDS.bed | bedtools sort | bedtools merge  > Zea_mays.AGPv4.34_coding_gene_nonCDS.bed

perform genome alignment using minimap2 and summarize the result

/usr/bin/time minimap2 -x asm5 -t 1 -a Zea_mays.AGPv4.dna.toplevel.fa Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa > minimap2_sorghum_5.sam 2> minimap2_asm5.log
/usr/bin/time minimap2 -x asm10 -t 1 -a Zea_mays.AGPv4.dna.toplevel.fa Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa > minimap2_sorghum_10.sam 2> minimap2_asm10.log
/usr/bin/time minimap2 -x asm20 -t 1 -a Zea_mays.AGPv4.dna.toplevel.fa Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa > minimap2_sorghum_20.sam 2> minimap2_asm20.log

samtools sort minimap2_sorghum_5.sam > minimap2_sorghum_5.bam
samtools sort minimap2_sorghum_10.sam > minimap2_sorghum_10.bam
samtools sort minimap2_sorghum_20.sam > minimap2_sorghum_20.bam


samtools depth minimap2_sorghum_5.bam | wc -l #2503314
samtools depth minimap2_sorghum_5.bam -b all_reproducible_peaks_summits_merged.bed | wc -l # 457652
samtools depth minimap2_sorghum_5.bam | awk '$3>0{print $0}' | wc -l #2489881
samtools depth minimap2_sorghum_5.bam -b all_reproducible_peaks_summits_merged.bed | awk '$3>0{print $0}' | wc -l #456029
samtools depth minimap2_sorghum_5.bam -b Zea_mays.AGPv4.34_coding_gene.bed | wc -l # 2332093
samtools depth minimap2_sorghum_5.bam -b Zea_mays.AGPv4.34_coding_gene.bed | awk '$3>0{print $0}' | wc -l # 2320162
samtools depth minimap2_sorghum_5.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | wc -l # 353993
samtools depth minimap2_sorghum_5.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | awk '$3>0{print $0}' | wc -l  # 348042


samtools depth minimap2_sorghum_10.bam | wc -l # 18445054
samtools depth minimap2_sorghum_10.bam -b all_reproducible_peaks_summits_merged.bed | wc -l # 3401133
samtools depth minimap2_sorghum_10.bam | awk '$3>0{print $0}' | wc -l # 17913090
samtools depth minimap2_sorghum_10.bam -b all_reproducible_peaks_summits_merged.bed | awk '$3>0{print $0}' | wc -l #3347285
samtools depth minimap2_sorghum_10.bam -b Zea_mays.AGPv4.34_coding_gene.bed | wc -l # 17619682
samtools depth minimap2_sorghum_10.bam -b Zea_mays.AGPv4.34_coding_gene.bed | awk '$3>0{print $0}' | wc -l # 17118348
samtools depth minimap2_sorghum_10.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | wc -l # 2354836
samtools depth minimap2_sorghum_10.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | awk '$3>0{print $0}' | wc -l  # 2259444



samtools depth minimap2_sorghum_20.bam | wc -l # 52224881
samtools depth minimap2_sorghum_20.bam -b all_reproducible_peaks_summits_merged.bed | wc -l   #11113820
samtools depth minimap2_sorghum_20.bam | awk '$3>0{print $0}' | wc -l # 49024035
samtools depth minimap2_sorghum_20.bam -b all_reproducible_peaks_summits_merged.bed | awk '$3>0{print $0}' | wc -l # 10680401
samtools depth minimap2_sorghum_20.bam -b Zea_mays.AGPv4.34_coding_gene.bed | wc -l # 47118588
samtools depth minimap2_sorghum_20.bam -b Zea_mays.AGPv4.34_coding_gene.bed | awk '$3>0{print $0}' | wc -l # 44182530
samtools depth minimap2_sorghum_20.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | wc -l # 7728976
samtools depth minimap2_sorghum_20.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | awk '$3>0{print $0}' | wc -l  # 7132545

perform genome alignment using LAST(http://last.cbrc.jp/) and summarize the result

/usr/bin/time sh ./lastalPipeline.sh >last.log 2>&1



python2 maf-convert sam sorghum_lastal.maf > sorghum_lastal.sam
sed -i 's/[0-9]\+H//g' sorghum_lastal.sam
samtools view -O BAM --reference Zea_mays.AGPv4.dna.toplevel.fa sorghum_lastal.sam | samtools sort - > sorghum_lastal.bam; samtools index sorghum_lastal.bam  # this is the many-to-many alignment


samtools depth sorghum_lastal.bam | wc -l # 597884081
samtools depth sorghum_lastal.bam | awk '$3>0{print $0}' | wc -l # 594220046



samtools depth sorghum_lastal.bam -b all_reproducible_peaks_summits_merged.bed | wc -l # 33554729
samtools depth sorghum_lastal.bam -b all_reproducible_peaks_summits_merged.bed | awk '$3>0{print $0}' | wc -l # 32598984
samtools depth sorghum_lastal.bam -b Zea_mays.AGPv4.34_coding_gene.bed | wc -l # 118438260
samtools depth sorghum_lastal.bam -b Zea_mays.AGPv4.34_coding_gene.bed | awk '$3>0{print $0}' | wc -l # 116732768
samtools depth sorghum_lastal.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | wc -l # 365399859
samtools depth sorghum_lastal.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | awk '$3>0{print $0}' | wc -l  # 364395540



python2 maf-convert sam sorghum_lastal_final.maf > sorghum_lastal_final.sam # this many to one
sed -i 's/query.//g' sorghum_lastal_final.sam
sed -i 's/col.//g' sorghum_lastal_final.sam
sed -i 's/[0-9]\+H//g' sorghum_lastal_final.sam
samtools view -O BAM --reference Zea_mays.AGPv4.dna.toplevel.fa sorghum_lastal_final.sam | samtools sort - > sorghum_lastal_final.bam; samtools index sorghum_lastal_final.bam
samtools depth sorghum_lastal_final.bam | wc -l  # 547513366
samtools depth sorghum_lastal_final.bam -b all_reproducible_peaks_summits_merged.bed | wc -l # 32593361
samtools depth sorghum_lastal_final.bam | awk '$3>0{print $0}' | wc -l  # 541455727
samtools depth sorghum_lastal_final.bam -b all_reproducible_peaks_summits_merged.bed | awk '$3>0{print $0}' | wc -l  #  31471791
samtools depth sorghum_lastal_final.bam -b Zea_mays.AGPv4.34_coding_gene.bed | wc -l # 113229899
samtools depth sorghum_lastal_final.bam -b Zea_mays.AGPv4.34_coding_gene.bed | awk '$3>0{print $0}' | wc -l # 110793737
samtools depth sorghum_lastal_final.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | wc -l # 337208689
samtools depth sorghum_lastal_final.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | awk '$3>0{print $0}' | wc -l  # 335012548



python2 maf-convert sam sorghum_lastal_final_split_swap_split_Comparator.maf > sorghum_lastal_final_split_swap_split_Comparator.sam  # one-to-one
sed -i 's/query.//g' sorghum_lastal_final_split_swap_split_Comparator.sam
sed -i 's/col.//g' sorghum_lastal_final_split_swap_split_Comparator.sam
sed -i 's/[0-9]\+H//g' sorghum_lastal_final_split_swap_split_Comparator.sam
samtools view -O BAM --reference Zea_mays.AGPv4.dna.toplevel.fa sorghum_lastal_final_split_swap_split_Comparator.sam | samtools sort - > sorghum_lastal_final_split_swap_split_Comparator.bam; samtools index sorghum_lastal_final_split_swap_split_Comparator.bam

samtools depth sorghum_lastal_final_split_swap_split_Comparator.bam | wc -l # 129898533
samtools depth sorghum_lastal_final_split_swap_split_Comparator.bam -b all_reproducible_peaks_summits_merged.bed | wc -l # 23170062
samtools depth sorghum_lastal_final_split_swap_split_Comparator.bam | awk '$3>0{print $0}' | wc -l # 127859061
samtools depth sorghum_lastal_final_split_swap_split_Comparator.bam -b all_reproducible_peaks_summits_merged.bed | awk '$3>0{print $0}' | wc -l  # 22483132
samtools depth sorghum_lastal_final_split_swap_split_Comparator.bam -b Zea_mays.AGPv4.34_coding_gene.bed | wc -l # 62730883
samtools depth sorghum_lastal_final_split_swap_split_Comparator.bam -b Zea_mays.AGPv4.34_coding_gene.bed | awk '$3>0{print $0}' | wc -l # 61595170
samtools depth sorghum_lastal_final_split_swap_split_Comparator.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | wc -l #  46540254
samtools depth sorghum_lastal_final_split_swap_split_Comparator.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | awk '$3>0{print $0}' | wc -l  # 46169395

perform genome alignment using AnchorWave and summarize the result

/usr/bin/time ./code/anchorwave proali -i Zea_mays.AGPv4.34.gff3 -as cds.fa -r Zea_mays.AGPv4.dna.toplevel.fa -a cds.sam -ar ref.sam -s Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa -n anchorwave.anchors -R 1 -Q 2 -o anchorwave.maf -f anchorwave.f.maf -w 38000 -fa3 200000 -B -4 -O1 -4 -E1 -2 -O2 -80 -E2 -1 -t 1 >anchorwave.log 2>&1


maf-convert sam anchorwave.maf | sed 's/Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa.//g' | sed 's/Zea_mays.AGPv4.dna.toplevel.fa.//g' | samtools view -O BAM --reference Zea_mays.AGPv4.dna.toplevel.fa - | samtools sort - > anchorwave.bam
samtools mpileup anchorwave.bam | wc -l   # 1870770572
samtools depth anchorwave.bam -b all_reproducible_peaks_summits_merged.bed | wc -l  # 57164808
samtools depth anchorwave.bam | awk '$3>0{print $0}' | wc -l  # 125848664
samtools depth anchorwave.bam -b all_reproducible_peaks_summits_merged.bed | awk '$3>0{print $0}' | wc -l    # 34133000
samtools depth anchorwave.bam -b Zea_mays.AGPv4.34_coding_gene.bed | wc -l # 150034551
samtools depth anchorwave.bam -b Zea_mays.AGPv4.34_coding_gene.bed | awk '$3>0{print $0}' | wc -l #68301524
samtools depth anchorwave.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | wc -l # 1308576282
samtools depth anchorwave.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | awk '$3>0{print $0}' | wc -l  # 27551272

perform genome alignment using MUMmer4 vRC1 (https://mummer4.github.io/) and summarize the result

 
#/usr/bin/time /home/bs674/software/bin/nucmer -t 60 --sam-short=mumer.maize.short.sam Zea_mays.AGPv4.dna.toplevel.fa Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa > nucmer.log 2>&1
 
#/usr/bin/time /home/bs674/software/bin/nucmer -t 69 --sam-long=mumer.maize.long.sam Zea_mays.AGPv4.dna.toplevel.fa Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa > nucmer.long.log 2>&1
/usr/bin/time /home/bs674/software/bin/nucmer -t 1 --sam-short=mumer.maize.short.sam Zea_mays.AGPv4.dna.toplevel.fa Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa > nucmer.log 2>&1
cat mumer.maize.short.sam | grep -v "@" | samtools view -O BAM --reference Zea_mays.AGPv4.dna.toplevel.fa - | samtools sort - > mumer.maize.short.bam
samtools index mumer.maize.short.bam
samtools depth mumer.maize.short.bam | wc -l   # 70953295
samtools depth mumer.maize.short.bam | awk '$3>0{print $0}' | wc -l  #69514918
samtools depth mumer.maize.short.bam -b all_reproducible_peaks_summits_merged.bed | wc -l # 16073863
samtools depth mumer.maize.short.bam -b all_reproducible_peaks_summits_merged.bed | awk '$3>0{print $0}' | wc -l # 15687267
samtools depth mumer.maize.short.bam -b Zea_mays.AGPv4.34_coding_gene.bed | wc -l # 61402015
samtools depth mumer.maize.short.bam -b Zea_mays.AGPv4.34_coding_gene.bed | awk '$3>0{print $0}' | wc -l # 60194318
samtools depth mumer.maize.short.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | wc -l # 10169800
samtools depth mumer.maize.short.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | awk '$3>0{print $0}' | wc -l  # 9987182

perform genome alignment using GSAlign(https://github.com/hsinnan75/GSAlign) and summarize the result

/usr/bin/time /home/bs674/software/GSAlign-1.0.22/bin/GSAlign -r Zea_mays.AGPv4.dna.toplevel.fa -q Sorghum_bicolor.Sorghum_bicolor_NCBIv3.dna.toplevel.fa -t 1 -o sorghum_gsalign -fmt 1 > GSAlign.log 2>&1
maf-convert sam sorghum_gsalign.maf | sed 's/qry.//g' | sed 's/ref.//g' | samtools view -O BAM --reference Zea_mays.AGPv4.dna.toplevel.fa - | samtools sort - > sorghum_gsalign.bam
samtools index sorghum_gsalign.bam
samtools depth sorghum_gsalign.bam -b all_reproducible_peaks_summits_merged.bed | wc -l # 5341494
samtools depth sorghum_gsalign.bam | wc -l  # 23870692
samtools depth sorghum_gsalign.bam | awk '$3>0{print $0}' | wc -l # 23114133
samtools depth sorghum_gsalign.bam -b all_reproducible_peaks_summits_merged.bed | awk '$3>0{print $0}' | wc -l  # 5138842
samtools depth sorghum_gsalign.bam -b Zea_mays.AGPv4.34_coding_gene.bed | wc -l #  21659744
samtools depth sorghum_gsalign.bam -b Zea_mays.AGPv4.34_coding_gene.bed | awk '$3>0{print $0}' | wc -l #21015569
samtools depth sorghum_gsalign.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | wc -l # 3322894
samtools depth sorghum_gsalign.bam -b B73.structuralTEv2.disjoined.2018-09-19.bed | awk '$3>0{print $0}' | wc -l  # 3214544

dat = read.table("all_reproducible_peaks_summits_merged.bed")
dat$len = dat$V3-dat$V2
sum(abs(dat$len))

## [1] 60036753

#60036753

orginaze all the numbers into a file names "summaryData’ manually and plot the result

TE2018 = read.table("B73.structuralTEv2.disjoined.2018-09-19.bed")
TE2018$len = TE2018$V3-TE2018$V2
TE2018TotalLength = sum(abs(TE2018$len)) # 1495364259

library(ggplot2)
data = read.table("summaryData", header=TRUE, sep="\t")
data$recall = data$depth_covered_bed / data$bed

data$non_TFBS_matches = data$depth_covered - data$depth_covered_bed
data$non_TFBS = data$depth - data$depth_bed


data$enrichment = (data$depth_covered_bed/data$depth_bed)/(data$non_TFBS_matches/data$non_TFBS)


data$approach <- factor(data$approach, levels = c("AnchorWave", "minimap2_asm5", "minimap2_asm10", "minimap2_asm20", "LAST many-to-many", "LAST many-to-one", "LAST one-to-one", "MUMmer4", "GSAlign"))
data$software <- factor(data$software, levels = c("AnchorWave", "minimap2", "LAST", "MUMmer4", "GSAlign"))
shape = c(16, 17, 17, 17,  15, 15, 15, 3, 7)

myColors <- c("#F8766D", "#D89000", "#A3A500", "#39B600", "#00BFC4", "#00B0F6", "#9590FF", "#E76BF3", "#FF62BC")
names(myColors) <- levels(data$approach)
colScale <- scale_colour_manual(name = "grp",values = myColors)
fillScale <- scale_fill_manual(name = "grp",values = myColors)

print(data)

##     software          approach      depth depth_bed depth_covered
## 1   minimap2     minimap2_asm5    2503314    457652       2489881
## 2   minimap2    minimap2_asm10   18445054   3401133      17913090
## 3   minimap2    minimap2_asm20   52224881  11113820      49024035
## 4    MUMmer4           MUMmer4   70953295  16073863      69514918
## 5    GSAlign           GSAlign   23870692   5341494      23114133
## 6       LAST LAST many-to-many  597884081  33554729     594220046
## 7       LAST  LAST many-to-one  547513366  32593361     541455727
## 8       LAST   LAST one-to-one  129898533  23170062     127859061
## 9 AnchorWave        AnchorWave 1870770572  57164808     125848664
##   depth_covered_bed      bed depth_genetic depth_genetic_covered X2018.09.19
## 1            456029 60036753       2332093               2320162      353993
## 2           3347285 60036753      17619682              17118348     2354836
## 3          10680401 60036753      47118588              44182530     7728976
## 4          15687267 60036753      61402015              60194318    10169800
## 5           5138842 60036753      21659744              21015569     3322894
## 6          32598984 60036753     118438260             116732768   365399859
## 7          31471791 60036753     113229899             110793737   337208689
## 8          22483132 60036753      62730883              61595170    46540254
## 9          34133000 60036753     150034551              68301524  1308576282
##   X2018.09.19_covered      recall non_TFBS_matches   non_TFBS enrichment
## 1              348042 0.007595831          2033852    2045662  1.0022398
## 2             2259444 0.055753931         14565805   15043921  1.0164725
## 3             7132545 0.177897712         38343634   41111061  1.0303615
## 4             9987182 0.261294394         53827651   54879432  0.9950186
## 5             3214544 0.085594935         17975291   18529198  0.9917066
## 6           364395540 0.542983795        561621062  564329352  0.9762018
## 7           335012548 0.524208746        509983936  514920005  0.9749348
## 8            46169395 0.374489473        105375929  106728471  0.9828076
## 9            27551272 0.568535077         91715664 1813605764 11.8071501

p = ggplot(data=data, aes(x=recall, y=enrichment, color=approach, shape=software)) + geom_point(size=5, alpha=0.8) + 
guides(colour = guide_legend(override.aes = list(shape = shape))) +scale_shape(guide = FALSE)+ colScale+fillScale+
  labs(x="TFBS recall", y="Match ratio in TFBS\nalignment to match ratio\n in non-TFBS alignment", title="")+ xlim(0, 1) + ylim(0, 12) +
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
              legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
            axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))

print(p)

file = "TFBS_quality_control"
png(paste(file, ".png", sep=""), width=720, height=560)
print(p)
dev.off()

## png 
##   2

pdf(paste(file, ".pdf", sep=""), width=9, height=7)
print(p)
dev.off()

## png 
##   2

Define the “recall” as (# position-match base pairs in TFBS and coding genetic region)/(total TFBS length + coding genetic region) By “coding genetic region”, I mean from TSS to TTS of coding genes (inlcuding introns, UTR, CDS) Define “precision” as (# position-match base pairs in TFBS and coding genetic region)/(# position-match base pairs in TFBS and coding genetic region + # position-match base pairs in TE region)

This is not a very solid analysis. But did not find much more convincing way to do that.

data$TFBS_coding_genes_recall = (data$depth_covered_bed+data$depth_genetic_covered) / (data$bed+161046032)
data$TFBS_coding_genes_precision = (data$depth_covered_bed+data$depth_genetic_covered)/(data$depth_covered_bed+data$depth_genetic_covered + data$X2018.09.19_covered)
data$TFBS_coding_genes_fscore = 2*(data$TFBS_coding_genes_precision*data$TFBS_coding_genes_recall)/(data$TFBS_coding_genes_precision+data$TFBS_coding_genes_recall)

p = ggplot(data=data, aes(x=approach, y=TFBS_coding_genes_fscore)) + geom_bar(stat="identity", alpha=0.8)  +guides(color = FALSE, fill=FALSE)+
  labs(x="", y="F-score", title="TFBS+coding_genes") + colScale+fillScale+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
              legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
          axis.ticks.x = element_blank(),
        axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))
  
print(p)

p = ggplot(data=data, aes(x=TFBS_coding_genes_recall, y=TFBS_coding_genes_precision, color=software, shape=software)) + geom_point(alpha=0.8)  +
  labs(x="recall", y="precision", title="TFBS+coding_genes") + colScale+fillScale+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
              legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
          axis.ticks.x = element_blank(),
        axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))
  
print(p)

data$coding_genes_recall = (data$depth_genetic_covered) / (161046032)
data$coding_genes_precision = (data$depth_genetic_covered)/(data$depth_genetic_covered + data$X2018.09.19_covered)
data$coding_genes_fscore = 2*(data$coding_genes_precision*data$coding_genes_recall)/(data$coding_genes_precision+data$coding_genes_recall)

p = ggplot(data=data, aes(x=approach, y=coding_genes_fscore)) + geom_bar(stat="identity", alpha=0.8)  +guides(color = FALSE, fill=FALSE)+
  labs(x="", y="F-score", title="coding_genes") + colScale+fillScale+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
              legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
          axis.ticks.x = element_blank(),
        axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))
  
print(p)

p = ggplot(data=data, aes(x=coding_genes_recall, y=coding_genes_precision, color=software, shape=software)) + geom_point(alpha=0.8)  +guides(color = FALSE, fill=FALSE)+
  labs(x="recall", y="precision", title="coding_genes") + colScale+fillScale+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
              legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
          axis.ticks.x = element_blank(),
        axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))
  
print(p)

data$TFBS_recall = (data$depth_covered_bed) / (data$bed)
data$TFBS_precision = (data$depth_covered_bed)/(data$depth_covered_bed + data$X2018.09.19_covered)
data$TFBS_fscore = 2*(data$TFBS_precision*data$TFBS_recall)/(data$TFBS_precision+data$TFBS_recall)

p = ggplot(data=data, aes(x=approach, y=TFBS_fscore)) + geom_bar(stat="identity", alpha=0.8)+
  labs(x="", y="F-score", title="TFBS") + colScale+fillScale+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
              legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
          axis.ticks.x = element_blank(),
        axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))
  
print(p)

p = ggplot(data=data, aes(x=TFBS_recall, y=TFBS_precision, color=software, shape=software)) + geom_point(alpha=0.8)  +
  labs(x="recall", y="precision", title="TFBS") + colScale+fillScale+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
              legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
          axis.ticks.x = element_blank(),
        axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))
  
print(p)

p = ggplot(data=data, aes(x=approach, y=recall, color=approach, fill=approach)) + geom_bar(stat="identity", alpha=0.8)  +guides(color = FALSE, fill=FALSE)+
  labs(x="", y="Proportion of maize\nTFBS matched to\nsorghum genome", title="") + colScale+fillScale+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
              legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
          axis.text.x = element_blank(),
          axis.ticks.x = element_blank())
  
  
        
#           axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))

print(p)

file = "TFBS_recall"
png(paste(file, ".png", sep=""), width=720, height=320)
print(p)
dev.off()

## png 
##   2

pdf(paste(file, ".pdf", sep=""), width=9, height=4)
print(p)
dev.off()

## png 
##   2

totalTELength = read.table("B73.structuralTEv2.disjoined.2018-09-19.bed")
totalTELength = sum(abs(totalTELength$V3-totalTELength$V2))

totalGenomeLength = read.table("Zea_mays.AGPv4.dna.toplevel.fa.fai")
totalGenomeLength = sum(totalGenomeLength$V2)
data$coverage = data$depth/totalGenomeLength


dat = rbind( data.frame(approach=data$approach, proportion=data$depth_covered/totalGenomeLength, category = "position match"), data.frame(approach=data$approach, proportion=(data$depth-data$depth_covered)/totalGenomeLength, category = "gap"),  data.frame(approach=data$approach, proportion=(totalGenomeLength-data$depth)/totalGenomeLength, category = "unaligned") )

dat$category = factor(dat$category, levels=c("unaligned", "gap", "position match"))
p = ggplot(data=dat, aes(x=approach, y=proportion, fill=category)) + geom_bar(stat="identity")  + scale_fill_manual(values=c("#54AEE1", "#92A000", "#EF8600")) + guides(fill=guide_legend(nrow=1,byrow=TRUE))+
  labs(x="", y="Proportion of maize \n genome aligned to sorghum", title="")+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
        legend.title = element_blank(),
              axis.line = element_blank(),
          legend.position = "top",
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
            axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))

print(p)

file = "maize_sorghum_genome_aligned"
png(paste(file, ".png", sep=""), width=720, height=560)
print(p)
dev.off()

## png 
##   2

pdf(paste(file, ".pdf", sep=""), width=9, height=7)
print(p)
dev.off()

## png 
##   2

dat = rbind( data.frame(approach=data$approach, proportion=data$depth_covered/totalGenomeLength, category = "position match"), data.frame(approach=data$approach, proportion=(data$depth-data$depth_covered)/totalGenomeLength, category = "gap"),  data.frame(approach=data$approach, proportion=(totalGenomeLength-data$depth)/totalGenomeLength, category = "unaligned") )


dat = rbind( 
data.frame(approach=data$approach, proportion=(data$depth_covered-data$X2018.09.19_covered)/totalGenomeLength, c="6non-TE position match", category = "position match", region="Non-TE_PAV"), 
data.frame(approach=data$approach, proportion=data$X2018.09.19_covered/totalGenomeLength, c="5TE position match", category = "position match", region="TE_PAV"), 
data.frame(approach=data$approach, proportion=(data$depth-data$depth_covered - data$X2018.09.19  + data$X2018.09.19_covered)/totalGenomeLength, c="3non-TE gap", category = "gap", region="Non-TE_PAV"),  
data.frame(approach=data$approach, proportion=(data$X2018.09.19-data$X2018.09.19_covered)/totalGenomeLength, c="4TE gap", category = "gap", region="TE_PAV"),
data.frame(approach=data$approach, proportion=(totalGenomeLength-data$depth-totalTELength+data$X2018.09.19)/totalGenomeLength, c="2non-TE unaligned", category = "unaligned", region="Non-TE_PAV"),
data.frame(approach=data$approach, proportion=(totalTELength-data$X2018.09.19)/totalGenomeLength, c="1TE unaligned", category = "unaligned", region="TE_PAV") )

dat$category = factor(dat$category, levels=c("unaligned", "gap", "position match"))
dat$c = factor(dat$c, levels=c("1TE unaligned", "2non-TE unaligned", "3non-TE gap", "4TE gap", "5TE position match", "6non-TE position match"))
dat$region = factor(dat$region)
print(dat)

##             approach   proportion                      c       category
## 1      minimap2_asm5 1.003164e-03 6non-TE position match position match
## 2     minimap2_asm10 7.331633e-03 6non-TE position match position match
## 3     minimap2_asm20 1.962054e-02 6non-TE position match position match
## 4            MUMmer4 2.788076e-02 6non-TE position match position match
## 5            GSAlign 9.320288e-03 6non-TE position match position match
## 6  LAST many-to-many 1.076420e-01 6non-TE position match position match
## 7   LAST many-to-one 9.669094e-02 6non-TE position match position match
## 8    LAST one-to-one 3.826065e-02 6non-TE position match position match
## 9         AnchorWave 4.603914e-02 6non-TE position match position match
## 10     minimap2_asm5 1.630110e-04     5TE position match position match
## 11    minimap2_asm10 1.058246e-03     5TE position match position match
## 12    minimap2_asm20 3.340641e-03     5TE position match position match
## 13           MUMmer4 4.677655e-03     5TE position match position match
## 14           GSAlign 1.505583e-03     5TE position match position match
## 15 LAST many-to-many 1.706704e-01     5TE position match position match
## 16  LAST many-to-one 1.569084e-01     5TE position match position match
## 17   LAST one-to-one 2.162417e-02     5TE position match position match
## 18        AnchorWave 1.290408e-02     5TE position match position match
## 19     minimap2_asm5 3.504313e-06            3non-TE gap            gap
## 20    minimap2_asm10 2.044754e-04            3non-TE gap            gap
## 21    minimap2_asm20 1.219819e-03            3non-TE gap            gap
## 22           MUMmer4 5.881546e-04            3non-TE gap            gap
## 23           GSAlign 3.035990e-04            3non-TE gap            gap
## 24 LAST many-to-many 1.245720e-03            3non-TE gap            gap
## 25  LAST many-to-one 1.808594e-03            3non-TE gap            gap
## 26   LAST one-to-one 7.815214e-04            3non-TE gap            gap
## 27        AnchorWave 2.172735e-01            3non-TE gap            gap
## 28     minimap2_asm5 2.787245e-06                4TE gap            gap
## 29    minimap2_asm10 4.467836e-05                4TE gap            gap
## 30    minimap2_asm20 2.793479e-04                4TE gap            gap
## 31           MUMmer4 8.553204e-05                4TE gap            gap
## 32           GSAlign 5.074744e-05                4TE gap            gap
## 33 LAST many-to-many 4.703887e-04                4TE gap            gap
## 34  LAST many-to-one 1.028597e-03                4TE gap            gap
## 35   LAST one-to-one 1.736977e-04                4TE gap            gap
## 36        AnchorWave 5.999884e-01                4TE gap            gap
## 37     minimap2_asm5 2.986158e-01      2non-TE unaligned      unaligned
## 38    minimap2_asm10 2.920863e-01      2non-TE unaligned      unaligned
## 39    minimap2_asm20 2.787821e-01      2non-TE unaligned      unaligned
## 40           MUMmer4 2.711535e-01      2non-TE unaligned      unaligned
## 41           GSAlign 2.899986e-01      2non-TE unaligned      unaligned
## 42 LAST many-to-many 1.907348e-01      2non-TE unaligned      unaligned
## 43  LAST many-to-one 2.011229e-01      2non-TE unaligned      unaligned
## 44   LAST one-to-one 2.605803e-01      2non-TE unaligned      unaligned
## 45        AnchorWave 3.630983e-02      2non-TE unaligned      unaligned
## 46     minimap2_asm5 7.002118e-01          1TE unaligned      unaligned
## 47    minimap2_asm10 6.992746e-01          1TE unaligned      unaligned
## 48    minimap2_asm20 6.967576e-01          1TE unaligned      unaligned
## 49           MUMmer4 6.956144e-01          1TE unaligned      unaligned
## 50           GSAlign 6.988212e-01          1TE unaligned      unaligned
## 51 LAST many-to-many 5.292367e-01          1TE unaligned      unaligned
## 52  LAST many-to-one 5.424405e-01          1TE unaligned      unaligned
## 53   LAST one-to-one 6.785797e-01          1TE unaligned      unaligned
## 54        AnchorWave 8.748511e-02          1TE unaligned      unaligned
##        region
## 1  Non-TE_PAV
## 2  Non-TE_PAV
## 3  Non-TE_PAV
## 4  Non-TE_PAV
## 5  Non-TE_PAV
## 6  Non-TE_PAV
## 7  Non-TE_PAV
## 8  Non-TE_PAV
## 9  Non-TE_PAV
## 10     TE_PAV
## 11     TE_PAV
## 12     TE_PAV
## 13     TE_PAV
## 14     TE_PAV
## 15     TE_PAV
## 16     TE_PAV
## 17     TE_PAV
## 18     TE_PAV
## 19 Non-TE_PAV
## 20 Non-TE_PAV
## 21 Non-TE_PAV
## 22 Non-TE_PAV
## 23 Non-TE_PAV
## 24 Non-TE_PAV
## 25 Non-TE_PAV
## 26 Non-TE_PAV
## 27 Non-TE_PAV
## 28     TE_PAV
## 29     TE_PAV
## 30     TE_PAV
## 31     TE_PAV
## 32     TE_PAV
## 33     TE_PAV
## 34     TE_PAV
## 35     TE_PAV
## 36     TE_PAV
## 37 Non-TE_PAV
## 38 Non-TE_PAV
## 39 Non-TE_PAV
## 40 Non-TE_PAV
## 41 Non-TE_PAV
## 42 Non-TE_PAV
## 43 Non-TE_PAV
## 44 Non-TE_PAV
## 45 Non-TE_PAV
## 46     TE_PAV
## 47     TE_PAV
## 48     TE_PAV
## 49     TE_PAV
## 50     TE_PAV
## 51     TE_PAV
## 52     TE_PAV
## 53     TE_PAV
## 54     TE_PAV

library(ggpattern)

## 
## Attaching package: 'ggpattern'

## The following objects are masked from 'package:ggplot2':
## 
##     flip_data, flipped_names, gg_dep, has_flipped_aes, remove_missing,
##     should_stop, waiver

p = ggplot(data=dat, aes(x=approach, y=proportion, fill=c, pattern = region)) + geom_bar(stat="identity")  + scale_fill_manual(values=c("#54AEE1","#54AEE1", "#92A000",  "#92A000", "#EF8600", "#EF8600")) + guides(fill=guide_legend(nrow=1,byrow=TRUE))+
  labs(x="", y="Proportion of maize \n genome aligned to sorghum", title="")+
   geom_bar_pattern( stat="identity", color = "black",  pattern_fill = "black",
                   pattern_angle = 45,
                   pattern_density = 0.1,
                   pattern_spacing = 0.025,
                   pattern_key_scale_factor = 0.6) +
                   scale_pattern_manual(values = c(TE_PAV = "stripe", TE_PAV = "none")) +
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
        legend.title = element_blank(),
              axis.line = element_blank(),
        legend.position = "top",
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
        
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
            axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))

print(p)

file = "maize_sorghum_genome_aligned_v2"
png(paste(file, ".png", sep=""), width=720, height=560)
print(p)
dev.off()

## png 
##   2

pdf(paste(file, ".pdf", sep=""), width=9, height=7)
print(p)
dev.off()

## png 
##   2

data0 = data.frame(software=c("ideal expection", as.character(data$software)), approach=c("ideal expection", as.character(data$approach)), coverage=c(0, (data$X2018.09.19_covered)/data$depth_covered) )

data0$approach <- factor(data0$approach, levels = c("ideal expection", "AnchorWave", "minimap2_asm5", "minimap2_asm10", "minimap2_asm20","LAST many-to-many", "LAST many-to-one", "LAST one-to-one", "MUMmer4", "GSAlign"))
data0$software <- factor(data0$software, levels = c("ideal expection", "AnchorWave", "minimap2", "LAST", "MUMmer4", "GSAlign"))


myColors <- c("#00FF00", "#F8766D", "#D89000", "#A3A500", "#39B600", "#00BFC4", "#00B0F6", "#9590FF", "#E76BF3", "#FF62BC")
names(myColors) <- levels(data$approach)
colScale <- scale_colour_manual(name = "grp",values = myColors)
fillScale <- scale_fill_manual(name = "grp",values = myColors)




p = ggplot(data=data0, aes(x=approach, y=coverage, color=approach, fill=approach)) + geom_bar(stat="identity", alpha=0.8)  +guides(color = FALSE, fill = FALSE)+
  labs(x="", y="Proportion of maize-sorghum\ngenome alignmenet matchs\nin maize TE region", title="")+ylim(0, 1)+colScale+fillScale+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
        legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
            axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = c("#00FF00", "#000000", "#000000", "#000000", "#000000", "#000000", "#000000", "#000000", "#000000", "#000000", "#000000")))

print(p)

file = "2018.09.19_te_depth"
png(paste(file, ".png", sep=""), width=720, height=560)
print(p)
dev.off()

## png 
##   2

pdf(paste(file, ".pdf", sep=""), width=9, height=7)
print(p)
dev.off()

## png 
##   2

data$X2018.09.19_gaps = data$X2018.09.19 - data$X2018.09.19_covered
data$total_gaps = data$depth - data$depth_covered
data$non_TE_gaps = data$total_gaps - data$X2018.09.19_gaps
data$non_TE_matches = data$depth_covered - data$X2018.09.19_covered
data$non_TE = data$depth - data$X2018.09.19

data0 = data.frame(software=data$software, approach=data$approach, coverage=((data$X2018.09.19_gaps/data$X2018.09.19) / (data$non_TE_gaps/data$non_TE ) ))  # the value of 162494287 is the total genetic region length, which is counted above in this file

p = ggplot(data=data0, aes(x=approach, y=coverage, color=approach, fill=approach)) +  geom_bar(stat="identity", alpha=0.8)  +guides(color = FALSE, fill = FALSE)+ylim(0, 5)+geom_hline(yintercept=1, linetype="dashed", color = "gray", size=1) +
  labs(x="", y="Gap ratio in TE\nalignment to gap ratio\nin non-TE alignment", title="")+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
        legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
            axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))

print(p)

file = "2018.09.19_te_gap_ratio"
png(paste(file, ".png", sep=""), width=720, height=560)
print(p)
dev.off()

## png 
##   2

pdf(paste(file, ".pdf", sep=""), width=9, height=7)
print(p)
dev.off()

## png 
##   2

data0 = data.frame(software=data$software, approach=data$approach, coverage=( (data$non_TE_gaps/data$non_TE ) ))  # the value of 162494287 is the total genetic region length, which is counted above in this file

p = ggplot(data=data0, aes(x=approach, y=coverage, color=approach, fill=approach)) +  geom_bar(stat="identity", alpha=0.8)  +guides(color = FALSE, fill = FALSE)+ylim(0, 1.1)+geom_hline(yintercept=1, linetype="dashed", color = "gray", size=1) +
  labs(x="", y="Gap ratio in non-TE", title="")+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
        legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
          axis.text.x = element_blank(),
          axis.ticks.x = element_blank())

#           axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))

print(p)

data0 = data.frame(software=data$software, approach=data$approach, coverage=( (data$X2018.09.19_gaps/data$X2018.09.19 ) ))  # the value of 162494287 is the total genetic region length, which is counted above in this file

p = ggplot(data=data0, aes(x=approach, y=coverage, color=approach, fill=approach)) +  geom_bar(stat="identity", alpha=0.8)  +guides(color = FALSE, fill = FALSE)+ylim(0, 1.1)+geom_hline(yintercept=1, linetype="dashed", color = "gray", size=1) +
  labs(x="", y="Gap ratio in TE", title="")+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
        legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
          axis.text.x = element_blank(),
          axis.ticks.x = element_blank())

#           axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))

print(p)

data0 = data.frame(software=data$software, approach=data$approach, coverage=((data$X2018.09.19_covered/data$X2018.09.19) / (data$non_TE_matches/data$non_TE ) ))  # the value of 162494287 is the total genetic region length, which is counted above in this file
p = ggplot(data=data0, aes(x=approach, y=coverage, color=approach, fill=approach)) +  geom_bar(stat="identity", alpha=0.8)  +guides(color = FALSE, fill = FALSE)+ylim(0, 1.1)+geom_hline(yintercept=1, linetype="dashed", color = "gray", size=1) +
  labs(x="", y="Match ratio in TE\nalignment to match ratio\n in non-TE alignment", title="")+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
        legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
          axis.text.x = element_blank(),
          axis.ticks.x = element_blank())

#           axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))

print(p)

file = "2018.09.19_te_match_ratio"
png(paste(file, ".png", sep=""), width=720, height=320)
print(p)
dev.off()

## png 
##   2

pdf(paste(file, ".pdf", sep=""), width=9, height=4)
print(p)
dev.off()

## png 
##   2

data0 = data.frame(software=data$software, approach=data$approach, ratio=((data$X2018.09.19-data$X2018.09.19_covered)/data$X2018.09.19), coverage=(data$X2018.09.19 - data$X2018.09.19_covered)/TE2018TotalLength )

p = ggplot(data=data0, aes(x=coverage, y=ratio, color=approach, shape=software)) + geom_point(size=5, alpha=0.8)  +
  guides(colour = guide_legend(override.aes = list(shape = shape))) +scale_shape(guide = FALSE)+
  labs(x="Gap ratio in maize TEs(recall) of\nmaize to sorghum genome alignment", y="Gap ratio in maize TE alignments\n(precision)", title="")+xlim(0,1)+ylim(0,1)+
            theme_bw() +theme_grey(base_size = 24) + theme(
        strip.background = element_blank(),
        strip.text.x = element_blank(),
        legend.title = element_blank(),
              axis.line = element_blank(),
            panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.border = element_blank(),
            panel.background = element_blank(),
            axis.text.y = element_text( colour = "black"),
            axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black"))

print(p)

file = "2018.09.19_te_depth_2"
png(paste(file, ".png", sep=""), width=720, height=560)
print(p)
dev.off()

## png 
##   2

pdf(paste(file, ".pdf", sep=""), width=9, height=7)
print(p)
dev.off()

## png 
##   2

# here I am using the Cairo library to compile the output plot. The output file looks better than native library, but it a little bit of mass up the Rmarkdown output file.

library(ggplot2)
library(compiler)
enableJIT(3)

## [1] 3

library(ggplot2)
library("Cairo")

changetoM <- function ( position ){
  position=position/1000000;
  paste(position, "M", sep="")
}



data =read.table("anchorwave.anchors", head=TRUE)
data$refChr = paste("chr", data$refChr, sep="")
data$queryChr = paste("chr", data$queryChr, sep="")
data = data[which(data$refChr %in% c("chr1", "chr2", "chr3", "chr4", "chr5", "chr6", "chr7", "chr8", "chr9", "chr10" )),]
data = data[which(data$queryChr %in% c("chr1", "chr2", "chr3", "chr4", "chr5", "chr6", "chr7", "chr8", "chr9", "chr10" )),]
data$refChr = factor(data$refChr, levels=c("chr1", "chr2", "chr3", "chr4", "chr5", "chr6", "chr7", "chr8", "chr9", "chr10" ))
data$queryChr = factor(data$queryChr, levels=c("chr1", "chr2", "chr3", "chr4", "chr5", "chr6", "chr7", "chr8", "chr9", "chr10" ))

data$strand = factor(data$strand)
data = data[which(data$gene != "intergenetic"),]
p = ggplot(data=data, aes(x=queryStart, y=referenceStart))+geom_point(size=2, aes(color=factor(strand)))+facet_grid(refChr~queryChr, scales="free", space="free" )+ theme_grey(base_size = 60) +
  labs(x="sorghum", y="maize")+scale_x_continuous(labels=changetoM) + scale_y_continuous(labels=changetoM) +
  theme(axis.line = element_blank(),
        panel.background = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        axis.text.y = element_text( colour = "black"),
        legend.position='none',
        axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black") )

CairoPNG(file="anchors.png",width = 4800, height = 3200)
p

dev.off()

## png 
##   2

CairoPDF(file="anchors.pdf",width = 60, height = 40)
p

dev.off()

## png 
##   2

data =read.table("anchorwave.anchors", head=TRUE)
data$refChr = paste("chr", data$refChr, sep="")
data$queryChr = paste("chr", data$queryChr, sep="")


data = data[which(data$refChr %in% c("chr4", "chr5")),]
data = data[which(data$queryChr %in% c("chr4", "chr5" )),]
data$refChr = factor(data$refChr, levels=c( "chr4", "chr5" ))
data$queryChr = factor(data$queryChr, levels=c( "chr4", "chr5" ))

data$strand = factor(data$strand)
data = data[which(data$gene != "intergenetic"),]
p = ggplot(data=data, aes(x=queryStart, y=referenceStart))+geom_point(size=2, aes(color=factor(strand)))+facet_grid(refChr~queryChr, scales="free", space="free" )+ theme_grey(base_size = 24) +
  labs(x="sorghum", y="maize")+scale_x_continuous(labels=changetoM) + scale_y_continuous(labels=changetoM) +
  theme(axis.line = element_blank(),
        panel.background = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        axis.text.y = element_text( colour = "black"),
        legend.position='none',
        axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black") )

CairoPNG(file="anchor2.png",width = 720, height = 560)
p

dev.off()

## png 
##   2

CairoPDF(file="anchor2.pdf",width = 9, height = 7)
p

dev.off()

## png 
##   2

# this plot suggested there is no Interchromosomal translocations  happened